Skill Adapter：通用技能的个性化修改与在线路由

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Abstract:Large language model (LLM) agents use external skills to reuse task procedures and experience with tools. General-purpose skills describe shared execution procedures, while specific tool choices, operating practices, and output requirements vary across users. In long-horizon tasks, subgoals, available inputs, and environmental states change as execution progresses. Agents must select currently applicable skills from an expanding skill pool and prepare candidates for subsequent steps.
We propose Skill Adapter, which enables personalized modification and online routing of general-purpose skills. The system distills local adjustments from user requirements and execution feedback and stores them as Adapters linked to general-purpose skills. The routing agent selects general-purpose skills and applicable Adapters based on the current subtask, execution progress, and environmental feedback, incrementally maintaining a task-level online skill library that the main LLM reads on demand. The system preloads candidates for subsequent nodes and rechecks their applicability once actual inputs are determined. Reinforcement learning optimizes the two types of decisions using modification gains and routing outcomes, respectively.
We evaluate Skill Adapter on ALFWorld, WebShop, and SkillsBench. Compared with SkillRL, it improves task success rates by an average of 11.43 percentage points and reduces mean execution time on all three benchmarks. Skill Adapter retains its performance advantage when the skill pool grows to 1,000 skills.

Comments:	9 pages, 3 figures, 3 tables
Subjects:	Artificial Intelligence (cs.AI)
Cite as:	arXiv:2611.17007 [cs.AI]
	(or arXiv:2611.17007v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2611.17007 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Qijia Zhuang [view email]
[v1] Thu, 12 Nov 2026 16:05:23 UTC (1,117 KB)